AIMC Topic: Electroencephalography

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EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

Computer methods and programs in biomedicine Jun 20, 2023
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsych...
Electroencephalography Neural Networks, Computer Algorithms Humans
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FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.

Sensors (Basel, Switzerland) Jun 17, 2023
In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still ro...
Algorithms Emotions Artificial Intelligence Electroencephalography Recognition, Psychology
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Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

Journal of neural engineering Jun 16, 2023
The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process,...
Electrocorticography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Electroencephalography
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TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging.

IEEE transactions on cybernetics Jun 15, 2023
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those meth...
Sleep Sleep Stages Machine Learning Electroencephalography Neural Networks, Computer
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Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

IEEE transactions on cybernetics Jun 15, 2023
The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classificatio...
Electroencephalography Algorithms Hand Biomechanical Phenomena Deep Learning Brain-Computer Interfaces
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A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

IEEE transactions on cybernetics Jun 15, 2023
Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, th...
Electrocardiography Electroencephalography Neural Networks, Computer
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Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Journal of neural engineering Jun 5, 2023
Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease in the community draws an increasing graph from the past to the present. While the ADHD diagnosis i...
Humans Electroencephalography Attention Deficit Disorder with Hyperactivity Algorithms Deep Learning
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Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters May 29, 2023
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...
Random Forest Neural Networks, Computer Machine Learning Datasets as Topic Humans Depression Electroencephalography Depressive Disorder, Major Decision Trees Case-Control Studies
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Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Physical and engineering sciences in medicine May 28, 2023
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective ...
Humans Machine Learning Algorithms Deep Learning Electroencephalography Parkinson Disease
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Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

Sensors (Basel, Switzerland) May 28, 2023
It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usabilit...
User-Computer Interface Software Electroencephalography Humans Emotions User-Centered Design Deep Learning
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